{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/davidfoster/.virtualenvs/deepreinforcement/lib/python2.7/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# %matplotlib inline\n",
    "import numpy as np\n",
    "np.set_printoptions(suppress=True)\n",
    "\n",
    "from keras.models import clone_model\n",
    "\n",
    "from game import Game, GameState\n",
    "from agent import Agent, User\n",
    "from memory import Memory\n",
    "from model import NN, CNN, Residual_CNN, Residual_NN\n",
    "from loss import cemse_loss, _cemse, softmax_cross_entropy_with_logits\n",
    "import config\n",
    "from funcs import playMatches, playMatchesBetweenVersions, printLosses\n",
    "from shutil import copyfile\n",
    "\n",
    "from utils import setup_logger\n",
    "import loggers as lg\n",
    "\n",
    "import pickle\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U1Y4vs9Y26d69OxERERQWFpKcnExxcXFrb1YIIdqNphy5bwBubmD5OKD3hWk2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a8c9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'random' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-1fbdb1d31896>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0mlg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'===================='\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0mmemory_samp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mltmemory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ms\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmemory_samp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'random' is not defined"
     ]
    }
   ],
   "source": [
    "INITIAL_MODEL_VERSION = None\n",
    "INITIAL_MEMORY_VERSION =  '_raw_newActions' # None # \n",
    "INITIAL_MCTS_VERSION = None\n",
    "\n",
    "# dumped_memory = False\n",
    "\n",
    "lg.logger_main.info('=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*')\n",
    "lg.logger_main.info('=*=*=*=*=*=.      NEW LOG      =*=*=*=*=*')\n",
    "lg.logger_main.info('=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*')\n",
    "\n",
    "env = Game()\n",
    "state_size = len(env.gameState.binary)\n",
    "action_size = len(env.actionSpace)\n",
    "\n",
    "if INITIAL_MEMORY_VERSION == None:\n",
    "    memory = Memory(config.MEMORY_SIZE)\n",
    "    #pass\n",
    "else:\n",
    "    memory = pickle.load( open( \"./memory/memory\" + str(INITIAL_MEMORY_VERSION) + \".p\",   \"rb\" ) )\n",
    "    #pass\n",
    "\n",
    "if INITIAL_MODEL_VERSION != None:\n",
    "    copyfile('./models/' + env.name + '/config.py', './config.py')\n",
    "    import config\n",
    "\n",
    "# current_NN = NN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, env.state_size + 1,  env.action_size + 1, config.HIDDEN_NN_LAYERS)\n",
    "# best_NN = NN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, env.state_size + 1,   env.action_size + 1, config.HIDDEN_NN_LAYERS)\n",
    "\n",
    "#current_NN = CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) + env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "#best_NN = CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) +  env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "\n",
    "current_NN = Residual_CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) + env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "best_NN = Residual_CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) +  env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "\n",
    "\n",
    "# current_NN = Residual_NN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, env.state_size ,   env.action_size + 1, config.HIDDEN_NN_LAYERS)\n",
    "# best_NN = Residual_NN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, env.state_size,   env.action_size + 1, config.HIDDEN_NN_LAYERS)\n",
    "\n",
    "\n",
    "if INITIAL_MODEL_VERSION == None:\n",
    "    best_player_version = 0\n",
    "    best_NN.model.set_weights(current_NN.model.get_weights())\n",
    "    copyfile('./config.py', './models/' + env.name + '/config.py')\n",
    "else:\n",
    "    best_player_version  = INITIAL_MODEL_VERSION\n",
    "    m_tmp = best_NN.read(env.name, best_player_version)\n",
    "\n",
    "    current_NN.model.set_weights(m_tmp.get_weights())\n",
    "    best_NN.model.set_weights(m_tmp.get_weights())\n",
    "    \n",
    "\n",
    "\n",
    "best_NNs = [best_NN]\n",
    "\n",
    "current_player = Agent('current_player', env.state_size, env.action_size, config.MCTS_SIMS, config.CPUCT, current_NN)\n",
    "best_player = Agent('best_player', env.state_size, env.action_size, config.MCTS_SIMS, config.CPUCT, best_NN)\n",
    "user_player = User('player1', env.state_size, env.action_size)\n",
    "iteration = 0\n",
    "\n",
    "if INITIAL_MCTS_VERSION != None:\n",
    "    best_player.mcts = pickle.load( open( \"./mcts/mcts\" + str(INITIAL_MCTS_VERSION) + \".p\",   \"rb\" ) )\n",
    "    \n",
    "\n",
    "while 1:\n",
    "\n",
    "    iteration += 1\n",
    "    reload(lg)\n",
    "    reload(config)\n",
    "    \n",
    "    print('ITERATION NUMBER ' + str(iteration))\n",
    "    \n",
    "    lg.logger_main.info('BEST PLAYER VERSION: %d', best_player_version)\n",
    "    print('BEST PLAYER VERSION ' + str(best_player_version))\n",
    "\n",
    "    ######## SELF PLAY ########\n",
    "    print('SELF PLAYING ' + str(config.EPISODES) + ' EPISODES...')\n",
    "    _, memory, _, _ = playMatches(best_player, best_player, config.EPISODES, lg.logger_main, turns_until_tau0 = config.TURNS_UNTIL_TAU0, memory = memory)\n",
    " #   _, memory, _, _ = playMatches(user_player, best_player, config.EPISODES, lg.logger_main, turns_until_tau0 = config.TURNS_UNTIL_TAU0, memory = memory)\n",
    "    print('\\n')\n",
    "    \n",
    "    memory.clear_stmemory()\n",
    "    \n",
    "\n",
    "    if len(memory.ltmemory) >= config.MEMORY_SIZE:\n",
    "        \n",
    "#         if dumped_memory == False:\n",
    "#             pickle.dump( memory, open( \"./memory/memory_raw_newActions.p\", \"wb\" ) )\n",
    "#             dumped_memory = True\n",
    "\n",
    "        ######## RETRAINING ########\n",
    "        print('RETRAINING...')\n",
    "        current_player.replay(memory.ltmemory)\n",
    "        #printLosses(best_player, current_player, memory)\n",
    "        #printLosses(best_player, best_player, memory)\n",
    "\n",
    "        print('')\n",
    "\n",
    "        lg.logger_memory.info('====================')\n",
    "        lg.logger_memory.info('NEW MEMORIES')\n",
    "        lg.logger_memory.info('====================')\n",
    "        \n",
    "        memory_samp = random.sample(memory.ltmemory, 1000)\n",
    "        \n",
    "        for s in memory_samp:\n",
    "            current_value, current_probs, _ = current_player.get_preds(s['state'])\n",
    "            best_value, best_probs, _ = best_player.get_preds(s['state'])\n",
    "\n",
    "            lg.logger_memory.info('MCTS VALUE FOR %s: %f', s['playerTurn'], s['value'])\n",
    "            lg.logger_memory.info('CUR PRED VALUE FOR %s: %f', s['playerTurn'], current_value)\n",
    "            lg.logger_memory.info('BES PRED VALUE FOR %s: %f', s['playerTurn'], best_value)\n",
    "            lg.logger_memory.info('THE MCTS ACTION VALUES: %s', ['%.2f' % elem for elem in s['AV']]  )\n",
    "            lg.logger_memory.info('CUR PRED ACTION VALUES: %s', ['%.2f' % elem for elem in  current_probs])\n",
    "            lg.logger_memory.info('BES PRED ACTION VALUES: %s', ['%.2f' % elem for elem in  best_probs])\n",
    "            lg.logger_memory.info('ID: %s', s['state'].convertStateToId())\n",
    "            lg.logger_memory.info('INPUT TO MODEL: %s', current_player.model.convertToModelInput(s['state']))\n",
    "\n",
    "            s['state'].render(lg.logger_memory)\n",
    "            \n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        ######## TOURNEY ########\n",
    "        print('TOURNEY...')\n",
    "        scores, _, points, sp_scores = playMatches(best_player, current_player, config.EVAL_EPISODES, lg.logger_tourney, turns_until_tau0 = 0, memory = None)\n",
    "        print(scores)\n",
    "        print(sp_scores)\n",
    "        print(points)\n",
    "\n",
    "        print('\\n\\n')\n",
    "\n",
    "        if scores['current_player'] > scores['best_player'] * config.SCORING_THRESHOLD:\n",
    "            best_player_version = best_player_version + 1\n",
    "            best_NN.model.set_weights(current_NN.model.get_weights())\n",
    "            best_NNs.append(best_NN)\n",
    "            best_NN.write(env.name, best_player_version)\n",
    "\n",
    "\n",
    "#     ### FOR NON TOURNEY ASSESSED TRAINING\n",
    "#         best_player_version = best_player_version + 1\n",
    "#         best_NN.model.set_weights(current_NN.model.get_weights())\n",
    "#         best_NNs.append(best_NN)\n",
    "#         best_NN.write(env.name, best_player_version)\n",
    "\n",
    "    else:\n",
    "        print('MEMORY SIZE: ' + str(len(memory.ltmemory)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Thg4dGrtBajRJDC3cE4gFCxYQHBysBJm5kJc7d24HX+U1a9bE39/mUDMgIIDX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\nKioqKlWQ/webL7R/z7/4igAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a8c9710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "TOURNEY...\n",
      "1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  {'drawn': 20, 'current_player': 0, 'best_player': 0}\n",
      "{'nsp': 0, 'drawn': 20, 'sp': 0}\n",
      "{'current_player': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'best_player': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}\n",
      "\n",
      "\n",
      "\n",
      "ITERATION NUMBER 82\n",
      "BEST PLAYER VERSION 31\n",
      "SELF PLAYING 75 EPISODES...\n",
      "1  2  3  4  5  6  7  8  9  10  11  12  13  14  15  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39 "
     ]
    }
   ],
   "source": [
    "while 1:\n",
    "\n",
    "    iteration += 1\n",
    "    reload(lg)\n",
    "    reload(config)\n",
    "    \n",
    "    print('ITERATION NUMBER ' + str(iteration))\n",
    "    \n",
    "    lg.logger_main.info('BEST PLAYER VERSION: %d', best_player_version)\n",
    "    print('BEST PLAYER VERSION ' + str(best_player_version))\n",
    "\n",
    "    ######## SELF PLAY ########\n",
    "    print('SELF PLAYING ' + str(config.EPISODES) + ' EPISODES...')\n",
    "    _, memory, _, _ = playMatches(best_player, best_player, config.EPISODES, lg.logger_main, turns_until_tau0 = config.TURNS_UNTIL_TAU0, memory = memory)\n",
    " #   _, memory, _, _ = playMatches(user_player, best_player, config.EPISODES, lg.logger_main, turns_until_tau0 = config.TURNS_UNTIL_TAU0, memory = memory)\n",
    "    print('\\n')\n",
    "    \n",
    "    memory.clear_stmemory()\n",
    "    \n",
    "\n",
    "    if len(memory.ltmemory) >= config.MEMORY_SIZE:\n",
    "        \n",
    "#         if dumped_memory == False:\n",
    "#             pickle.dump( memory, open( \"./memory/memory_raw_newActions.p\", \"wb\" ) )\n",
    "#             dumped_memory = True\n",
    "\n",
    "        ######## RETRAINING ########\n",
    "        print('RETRAINING...')\n",
    "        current_player.replay(memory.ltmemory)\n",
    "        #printLosses(best_player, current_player, memory)\n",
    "        #printLosses(best_player, best_player, memory)\n",
    "\n",
    "        print('')\n",
    "\n",
    "        lg.logger_memory.info('====================')\n",
    "        lg.logger_memory.info('NEW MEMORIES')\n",
    "        lg.logger_memory.info('====================')\n",
    "        \n",
    "        memory_samp = random.sample(memory.ltmemory, 1000)\n",
    "        \n",
    "        for s in memory_samp:\n",
    "            current_value, current_probs, _ = current_player.get_preds(s['state'])\n",
    "            best_value, best_probs, _ = best_player.get_preds(s['state'])\n",
    "\n",
    "            lg.logger_memory.info('MCTS VALUE FOR %s: %f', s['playerTurn'], s['value'])\n",
    "            lg.logger_memory.info('CUR PRED VALUE FOR %s: %f', s['playerTurn'], current_value)\n",
    "            lg.logger_memory.info('BES PRED VALUE FOR %s: %f', s['playerTurn'], best_value)\n",
    "            lg.logger_memory.info('THE MCTS ACTION VALUES: %s', ['%.2f' % elem for elem in s['AV']]  )\n",
    "            lg.logger_memory.info('CUR PRED ACTION VALUES: %s', ['%.2f' % elem for elem in  current_probs])\n",
    "            lg.logger_memory.info('BES PRED ACTION VALUES: %s', ['%.2f' % elem for elem in  best_probs])\n",
    "            lg.logger_memory.info('ID: %s', s['state'].convertStateToId())\n",
    "            lg.logger_memory.info('INPUT TO MODEL: %s', current_player.model.convertToModelInput(s['state']))\n",
    "\n",
    "            s['state'].render(lg.logger_memory)\n",
    "            \n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        ######## TOURNEY ########\n",
    "        print('TOURNEY...')\n",
    "        scores, _, points, sp_scores = playMatches(best_player, current_player, config.EVAL_EPISODES, lg.logger_tourney, turns_until_tau0 = 0, memory = None)\n",
    "        print(scores)\n",
    "        print(sp_scores)\n",
    "        print(points)\n",
    "\n",
    "        print('\\n\\n')\n",
    "\n",
    "        if scores['current_player'] > scores['best_player'] * config.SCORING_THRESHOLD:\n",
    "            best_player_version = best_player_version + 1\n",
    "            best_NN.model.set_weights(current_NN.model.get_weights())\n",
    "            best_NNs.append(best_NN)\n",
    "            best_NN.write(env.name, best_player_version)\n",
    "\n",
    "\n",
    "#     ### FOR NON TOURNEY ASSESSED TRAINING\n",
    "#         best_player_version = best_player_version + 1\n",
    "#         best_NN.model.set_weights(current_NN.model.get_weights())\n",
    "#         best_NNs.append(best_NN)\n",
    "#         best_NN.write(env.name, best_player_version)\n",
    "\n",
    "    else:\n",
    "        print('MEMORY SIZE: ' + str(len(memory.ltmemory)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65604"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(memory.ltmemory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XzJo1K9sG5H379ikEf0ZGBllaWtKMGTN4nFzNMGvWLCJSC2H5izdz5ky6f/8+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\nE0bkci3iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x145be73d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-906b7a0c4b36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mcurrent_player\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmemory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mltmemory\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/davidfoster/GoogleDrive/AGI/app/agent.pyc\u001b[0m in \u001b[0;36mreplay\u001b[0;34m(self, ltmemory)\u001b[0m\n\u001b[1;32m    272\u001b[0m                 \u001b[0mdisplay\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m                 \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m                 \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m                 \u001b[0;31m#print('TRAIN OVERALL LOSS: ' + str(self.train_overall_loss))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "while 1:\n",
    "    current_player.replay(memory.ltmemory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 0\n",
      "0.7929292929292929\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.79292929 0.05555556 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.03535354 0.         0.\n",
      " 0.         0.03535354 0.         0.03535354 0.         0.\n",
      " 0.         0.         0.         0.04545455 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1\n",
      "0.7929292929292929\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.05555556\n",
      " 0.79292929 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.03535354 0.         0.03535354\n",
      " 0.         0.         0.         0.03535354 0.         0.04545455\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "4\n",
      "0.2040133779264214\n",
      "[0.         0.13712375 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.16722408 0.         0.\n",
      " 0.         0.         0.19063545 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.14381271 0.         0.15719064 0.         0.\n",
      " 0.         0.         0.         0.20401338 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "5\n",
      "0.2040133779264214\n",
      "[0.         0.         0.         0.         0.         0.13712375\n",
      " 0.         0.         0.         0.         0.         0.16722408\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.19063545 0.15719064 0.         0.14381271\n",
      " 0.         0.         0.         0.         0.         0.20401338\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "26\n",
      "0.6869565217391305\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.05217391\n",
      " 0.         0.         0.         0.06086957 0.         0.\n",
      " 0.         0.         0.         0.05217391 0.         0.\n",
      " 0.04347826 0.         0.06086957 0.         0.         0.\n",
      " 0.04347826 0.         0.         0.         0.68695652 0.        ]\n",
      "27\n",
      "0.6869565217391305\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.05217391\n",
      " 0.         0.         0.         0.05217391 0.         0.\n",
      " 0.         0.         0.         0.06086957 0.         0.\n",
      " 0.06086957 0.         0.04347826 0.         0.         0.\n",
      " 0.68695652 0.         0.         0.         0.04347826 0.        ]\n",
      "48\n",
      "0.9024390243902439\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.90243902 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.02439024 0.         0.02439024\n",
      " 0.02439024 0.         0.02439024 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "49\n",
      "0.9024390243902439\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.90243902 0.         0.\n",
      " 0.         0.         0.         0.02439024 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.02439024 0.         0.02439024 0.02439024 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "52\n",
      "0.22424242424242424\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.22424242 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.21818182\n",
      " 0.         0.         0.         0.18181818 0.         0.18787879\n",
      " 0.         0.         0.18787879 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "53\n",
      "0.22424242424242424\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.22424242 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.18181818 0.         0.\n",
      " 0.         0.21818182 0.         0.         0.         0.\n",
      " 0.18787879 0.         0.         0.18787879 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "56\n",
      "0.7861271676300579\n",
      "[0.         0.         0.         0.         0.         0.04046243\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.78612717 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.06936416 0.         0.05780347\n",
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      "990\n",
      "0.8383233532934131\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.83832335 0.         0.         0.\n",
      " 0.05389222 0.         0.04790419 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.01197605 0.         0.         0.0239521  0.         0.\n",
      " 0.0239521  0.         0.         0.         0.         0.        ]\n",
      "991\n",
      "0.8383233532934131\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.04790419 0.         0.05389222 0.\n",
      " 0.         0.         0.83832335 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.0239521\n",
      " 0.         0.         0.01197605 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.0239521  0.        ]\n",
      "992\n",
      "0.2719665271966527\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.27196653 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.15062762 0.         0.20502092 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.19246862 0.         0.         0.0292887  0.         0.\n",
      " 0.15062762 0.         0.         0.         0.         0.        ]\n",
      "993\n",
      "0.2719665271966527\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.27196653 0.20502092 0.         0.15062762 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.0292887\n",
      " 0.         0.         0.19246862 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.15062762 0.        ]\n",
      "996\n",
      "0.8142076502732241\n",
      "[0.04371585 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.03825137 0.         0.03278689 0.         0.         0.\n",
      " 0.         0.         0.81420765 0.         0.         0.\n",
      " 0.03825137 0.         0.         0.         0.         0.\n",
      " 0.03278689 0.         0.         0.         0.         0.        ]\n",
      "997\n",
      "0.8142076502732241\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.04371585 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03278689 0.         0.03825137 0.\n",
      " 0.         0.         0.         0.         0.81420765 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03825137 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.03278689 0.        ]\n",
      "1004\n",
      "0.6204379562043796\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.11678832 0.         0.         0.         0.         0.\n",
      " 0.10948905 0.         0.04379562 0.         0.         0.62043796\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.10948905 0.         0.         0.         0.         0.        ]\n",
      "1005\n",
      "0.6204379562043796\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.11678832 0.         0.         0.\n",
      " 0.         0.         0.04379562 0.         0.10948905 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.62043796 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.10948905 0.        ]\n",
      "1006\n",
      "0.8586956521739131\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.03804348 0.         0.         0.         0.85869565 0.\n",
      " 0.0326087  0.         0.03804348 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.0326087  0.         0.         0.         0.         0.        ]\n",
      "1007\n",
      "0.8586956521739131\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03804348 0.         0.         0.\n",
      " 0.         0.         0.03804348 0.         0.0326087  0.\n",
      " 0.85869565 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.0326087  0.        ]\n",
      "1008\n",
      "0.3852140077821012\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.18677043 0.         0.\n",
      " 0.16731518 0.         0.         0.         0.         0.\n",
      " 0.22178988 0.         0.03891051 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.38521401 0.         0.         0.         0.         0.        ]\n",
      "1009\n",
      "0.3852140077821012\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.16731518 0.         0.         0.18677043\n",
      " 0.         0.         0.03891051 0.         0.22178988 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.38521401 0.        ]\n",
      "1010\n",
      "0.8636363636363636\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.03535354 0.         0.\n",
      " 0.03030303 0.         0.         0.         0.         0.\n",
      " 0.04040404 0.         0.03030303 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.86363636\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1011\n",
      "0.8636363636363636\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03030303 0.         0.         0.03535354\n",
      " 0.         0.         0.03030303 0.         0.04040404 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.86363636 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1012\n",
      "0.4185185185185185\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.41851852 0.         0.\n",
      " 0.21851852 0.         0.         0.         0.         0.\n",
      " 0.17777778 0.         0.03703704 0.         0.14814815 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1013\n",
      "0.4185185185185185\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.21851852 0.         0.         0.41851852\n",
      " 0.         0.         0.03703704 0.         0.17777778 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.14814815 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1014\n",
      "0.44339622641509435\n",
      "[0.         0.         0.17924528 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.15566038 0.         0.         0.         0.         0.\n",
      " 0.18867925 0.         0.44339623 0.         0.03301887 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1015\n",
      "0.44339622641509435\n",
      "[0.         0.         0.         0.         0.17924528 0.\n",
      " 0.         0.         0.15566038 0.         0.         0.\n",
      " 0.         0.         0.44339623 0.         0.18867925 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03301887 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1016\n",
      "0.8549222797927462\n",
      "[0.         0.         0.03626943 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.03108808 0.85492228 0.         0.         0.         0.\n",
      " 0.04145078 0.         0.         0.         0.03626943 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1017\n",
      "0.8549222797927462\n",
      "[0.         0.         0.         0.         0.03626943 0.\n",
      " 0.         0.85492228 0.03108808 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.04145078 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03626943 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1018\n",
      "0.4128787878787879\n",
      "[0.         0.         0.18939394 0.         0.         0.\n",
      " 0.41287879 0.         0.         0.         0.         0.\n",
      " 0.17045455 0.         0.         0.         0.         0.\n",
      " 0.1969697  0.         0.         0.         0.03030303 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1019\n",
      "0.4128787878787879\n",
      "[0.41287879 0.         0.         0.         0.18939394 0.\n",
      " 0.         0.         0.17045455 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.1969697  0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03030303 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1020\n",
      "0.40865384615384615\n",
      "[0.         0.         0.09615385 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.32692308 0.         0.         0.         0.         0.\n",
      " 0.40865385 0.         0.         0.         0.16826923 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1021\n",
      "0.40865384615384615\n",
      "[0.         0.         0.         0.         0.09615385 0.\n",
      " 0.         0.         0.32692308 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.40865385 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.16826923 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1022\n",
      "0.4673913043478261\n",
      "[0.         0.         0.4673913  0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.31521739\n",
      " 0.17934783 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.03804348 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1023\n",
      "0.4673913043478261\n",
      "[0.         0.         0.         0.         0.4673913  0.\n",
      " 0.         0.         0.17934783 0.31521739 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03804348 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1024\n",
      "0.39572192513368987\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.36363636\n",
      " 0.39572193 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.24064171 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1025\n",
      "0.39572192513368987\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.39572193 0.36363636 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.24064171 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1026\n",
      "0.5738636363636364\n",
      "[0.         0.         0.         0.         0.         0.57386364\n",
      " 0.         0.         0.         0.         0.         0.375\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.05113636 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1027\n",
      "0.5738636363636364\n",
      "[0.         0.57386364 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.375      0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.05113636 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1028\n",
      "0.6268656716417911\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.62686567\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.37313433 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1029\n",
      "0.6268656716417911\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.62686567 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.37313433 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1030\n",
      "0.9466666666666667\n",
      "[0.         0.         0.         0.         0.94666667 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.05333333 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1031\n",
      "0.9466666666666667\n",
      "[0.         0.         0.94666667 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.05333333 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1032\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "1033\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "1034\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "1035\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "1036\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "1037\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "1054\n",
      "0.8666666666666667\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.86666667\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.02       0.         0.02       0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.02666667 0.02       0.02666667 0.         0.         0.02      ]\n",
      "1055\n",
      "0.8666666666666667\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.86666667 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.02       0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.02       0.02\n",
      " 0.         0.         0.02666667 0.02       0.02666667 0.        ]\n",
      "1072\n",
      "0.8461538461538461\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.03205128 0.         0.         0.03846154\n",
      " 0.05128205 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.84615385 0.         0.         0.03205128]\n",
      "1073\n",
      "0.8461538461538461\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.03205128 0.\n",
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      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1926\n",
      "0.6462585034013606\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.13605442 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.13605442 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.6462585  0.         0.\n",
      " 0.08163265 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1927\n",
      "0.6462585034013606\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.13605442 0.\n",
      " 0.         0.         0.         0.         0.13605442 0.\n",
      " 0.         0.         0.         0.6462585  0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.08163265 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1930\n",
      "0.34057971014492755\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.2173913  0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.34057971 0.         0.23550725 0.         0.         0.20652174\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1931\n",
      "0.34057971014492755\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.2173913  0.\n",
      " 0.         0.         0.23550725 0.         0.34057971 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.20652174 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1932\n",
      "0.927461139896373\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.02590674 0.02590674\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.02072539 0.         0.         0.92746114\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1933\n",
      "0.927461139896373\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.02590674 0.02590674 0.\n",
      " 0.         0.         0.02072539 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.92746114 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1944\n",
      "0.7209302325581395\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.03875969 0.03100775 0.\n",
      " 0.         0.03875969 0.06976744 0.         0.         0.\n",
      " 0.05426357 0.04651163 0.         0.         0.72093023 0.        ]\n",
      "1945\n",
      "0.7209302325581395\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.03875969 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.06976744 0.03875969 0.         0.         0.03100775 0.\n",
      " 0.72093023 0.         0.         0.04651163 0.05426357 0.        ]\n",
      "1954\n",
      "0.7793103448275862\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.03448276\n",
      " 0.         0.         0.02758621 0.         0.         0.\n",
      " 0.         0.04827586 0.         0.         0.03448276 0.\n",
      " 0.         0.         0.         0.77931034 0.         0.\n",
      " 0.02758621 0.04827586 0.         0.         0.         0.        ]\n",
      "1955\n",
      "0.7793103448275862\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.02758621 0.         0.         0.03448276\n",
      " 0.         0.         0.         0.         0.         0.04827586\n",
      " 0.         0.         0.         0.         0.         0.77931034\n",
      " 0.         0.         0.         0.         0.03448276 0.\n",
      " 0.         0.         0.         0.04827586 0.02758621 0.        ]\n",
      "1960\n",
      "0.18316831683168316\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.17821782\n",
      " 0.17326733 0.         0.17821782 0.18316832 0.         0.\n",
      " 0.         0.         0.1039604  0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.11386139 0.06930693 0.         0.         0.         0.        ]\n",
      "1961\n",
      "0.18316831683168316\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.17821782 0.         0.17326733 0.17821782\n",
      " 0.         0.         0.         0.         0.1039604  0.\n",
      " 0.         0.         0.         0.18316832 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.06930693 0.11386139 0.        ]\n",
      "1962\n",
      "0.47058823529411764\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.01470588 0.         0.         0.46323529\n",
      " 0.00735294 0.         0.01470588 0.         0.         0.\n",
      " 0.         0.         0.47058824 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.01470588 0.01470588 0.         0.         0.         0.        ]\n",
      "1963\n",
      "0.47058823529411764\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.01470588 0.         0.00735294 0.46323529\n",
      " 0.         0.         0.01470588 0.         0.47058824 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.01470588 0.01470588 0.        ]\n",
      "1986\n",
      "0.88\n",
      "[0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.88 0.   0.   0.\n",
      " 0.   0.   0.   0.   0.   0.   0.   0.02 0.   0.   0.   0.02 0.   0.\n",
      " 0.   0.02 0.02 0.   0.   0.   0.02 0.   0.   0.   0.   0.   0.02 0.  ]\n",
      "1987\n",
      "0.88\n",
      "[0.   0.   0.   0.   0.   0.   0.   0.   0.   0.   0.88 0.   0.   0.\n",
      " 0.   0.   0.   0.   0.   0.   0.   0.   0.   0.02 0.   0.   0.   0.02\n",
      " 0.02 0.   0.   0.   0.02 0.02 0.   0.   0.02 0.   0.   0.   0.   0.  ]\n",
      "1990\n",
      "0.2152777777777778\n",
      "[0.         0.         0.         0.09722222 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.21527778 0.         0.         0.14583333 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.15972222\n",
      " 0.1875     0.         0.         0.         0.16666667 0.\n",
      " 0.         0.         0.         0.         0.02777778 0.        ]\n",
      "1991\n",
      "0.2152777777777778\n",
      "[0.         0.         0.         0.09722222 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.21527778 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.14583333 0.16666667 0.\n",
      " 0.         0.         0.1875     0.15972222 0.         0.\n",
      " 0.02777778 0.         0.         0.         0.         0.        ]\n",
      "1994\n",
      "0.8366013071895425\n",
      "[0.         0.         0.         0.01960784 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.02614379\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.02614379 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.03267974\n",
      " 0.03267974 0.         0.         0.83660131 0.02614379 0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "1995\n",
      "0.8366013071895425\n",
      "[0.         0.         0.         0.01960784 0.         0.\n",
      " 0.         0.         0.         0.02614379 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.02614379 0.02614379 0.83660131\n",
      " 0.         0.         0.03267974 0.03267974 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2000\n",
      "0.8450704225352113\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.02112676\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.02816901 0.         0.\n",
      " 0.         0.         0.84507042 0.04225352 0.         0.03521127\n",
      " 0.02816901 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2001\n",
      "0.8450704225352113\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.02112676 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.04225352 0.84507042 0.\n",
      " 0.         0.         0.         0.02816901 0.         0.\n",
      " 0.         0.         0.02816901 0.03521127 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2002\n",
      "0.8227272727272728\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.03636364\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.02727273 0.         0.04090909 0.         0.\n",
      " 0.         0.         0.         0.82272727 0.         0.03636364\n",
      " 0.03636364 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2003\n",
      "0.8227272727272728\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.03636364 0.         0.\n",
      " 0.         0.         0.         0.02727273 0.         0.\n",
      " 0.         0.         0.         0.82272727 0.         0.\n",
      " 0.         0.         0.         0.04090909 0.         0.\n",
      " 0.         0.         0.03636364 0.03636364 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2006\n",
      "0.7970297029702971\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.03960396\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.03960396 0.03960396 0.04455446 0.         0.7970297\n",
      " 0.         0.         0.         0.         0.         0.03960396\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2007\n",
      "0.7970297029702971\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.03960396 0.         0.\n",
      " 0.         0.         0.03960396 0.03960396 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.7970297  0.         0.04455446 0.         0.\n",
      " 0.         0.         0.         0.03960396 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2010\n",
      "0.4267515923566879\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.14012739\n",
      " 0.12101911 0.         0.         0.         0.13375796 0.\n",
      " 0.         0.         0.05095541 0.42675159 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.12738854\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2011\n",
      "0.4267515923566879\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.12101911 0.14012739 0.         0.\n",
      " 0.         0.         0.05095541 0.         0.         0.\n",
      " 0.13375796 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.42675159 0.         0.\n",
      " 0.         0.         0.         0.12738854 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2012\n",
      "0.8012048192771084\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.03614458\n",
      " 0.04819277 0.         0.80120482 0.         0.03614458 0.\n",
      " 0.         0.         0.03614458 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.04216867\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2013\n",
      "0.8012048192771084\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.04819277 0.03614458 0.         0.\n",
      " 0.         0.         0.03614458 0.         0.         0.\n",
      " 0.03614458 0.         0.80120482 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.04216867 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2014\n",
      "0.22844827586206898\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.15086207 0.         0.         0.         0.22844828\n",
      " 0.18534483 0.         0.         0.         0.19827586 0.\n",
      " 0.         0.         0.04310345 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.19396552\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2015\n",
      "0.22844827586206898\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.18534483 0.22844828 0.         0.\n",
      " 0.         0.15086207 0.04310345 0.         0.         0.\n",
      " 0.19827586 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.19396552 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2018\n",
      "0.8757763975155279\n",
      "[0.         0.         0.         0.         0.02484472 0.8757764\n",
      " 0.         0.02484472 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.01863354 0.\n",
      " 0.         0.         0.0310559  0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.02484472\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2019\n",
      "0.8757763975155279\n",
      "[0.         0.8757764  0.02484472 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.02484472 0.0310559  0.         0.         0.\n",
      " 0.01863354 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.02484472 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2022\n",
      "0.8142076502732241\n",
      "[0.         0.         0.         0.         0.04371585 0.\n",
      " 0.         0.04918033 0.         0.         0.         0.\n",
      " 0.         0.81420765 0.         0.         0.04371585 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.04918033\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2023\n",
      "0.8142076502732241\n",
      "[0.         0.         0.04371585 0.         0.         0.\n",
      " 0.         0.81420765 0.         0.         0.         0.\n",
      " 0.         0.04918033 0.         0.         0.         0.\n",
      " 0.04371585 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.04918033 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2028\n",
      "0.36645962732919257\n",
      "[0.         0.         0.         0.         0.25465839 0.\n",
      " 0.         0.34161491 0.         0.36645963 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.03726708\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2029\n",
      "0.36645962732919257\n",
      "[0.         0.         0.25465839 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.36645963\n",
      " 0.         0.34161491 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.03726708 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2030\n",
      "0.34177215189873417\n",
      "[0.         0.         0.34177215 0.         0.31012658 0.\n",
      " 0.         0.10126582 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.24683544\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2031\n",
      "0.34177215189873417\n",
      "[0.         0.         0.31012658 0.         0.34177215 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.10126582 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.24683544 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2032\n",
      "0.8104575163398693\n",
      "[0.         0.         0.         0.         0.81045752 0.\n",
      " 0.         0.14379085 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.04575163\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2033\n",
      "0.8104575163398693\n",
      "[0.         0.         0.81045752 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.14379085 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.04575163 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2034\n",
      "0.6367713004484304\n",
      "[0.        0.        0.        0.        0.        0.        0.\n",
      " 0.6367713 0.        0.        0.        0.        0.        0.\n",
      " 0.        0.        0.        0.        0.        0.        0.\n",
      " 0.        0.        0.        0.        0.        0.        0.\n",
      " 0.        0.3632287 0.        0.        0.        0.        0.\n",
      " 0.        0.        0.        0.        0.        0.        0.       ]\n",
      "2035\n",
      "0.6367713004484304\n",
      "[0.        0.        0.        0.        0.        0.        0.\n",
      " 0.        0.        0.        0.        0.        0.        0.6367713\n",
      " 0.        0.        0.        0.        0.        0.        0.\n",
      " 0.        0.        0.        0.        0.        0.        0.\n",
      " 0.        0.        0.        0.        0.        0.3632287 0.\n",
      " 0.        0.        0.        0.        0.        0.        0.       ]\n",
      "2036\n",
      "0.9588477366255144\n",
      "[0.95884774 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.04115226\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2037\n",
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      "[0.         0.         0.         0.         0.         0.\n",
      " 0.95884774 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.04115226 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.        ]\n",
      "2038\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "2039\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "2040\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "2041\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "2042\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "2043\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "2046\n",
      "1.0\n",
      "[0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "2047\n",
      "1.0\n",
      "[0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.\n",
      " 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "for m_idx, m in enumerate(memory.ltmemory):\n",
    "    gs = m['state']\n",
    "    AV = m['AV']\n",
    "    input_mat = current_player.model.convertToModelInput(gs)\n",
    "    \n",
    "    #best_value, best_probs, _ = current_player.get_preds(gs)\n",
    "    #if any(i > 0.7 and i < 1 and np.sum(input_mat) < 20 for i in best_probs):\n",
    "    if gs.checkForThreat() == 1:\n",
    "        print(m_idx)\n",
    "        print(np.max(AV))\n",
    "        print (AV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
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    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-8f1378eaff36>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#best_value, best_probs, _ = current_player.get_preds(gs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m#if any(i > 0.7 and i < 1 and np.sum(input_mat) < 20 for i in best_probs):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.7\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_mat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m20\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mAV\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mgs_tmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mAV_tmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-9-8f1378eaff36>\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m((i,))\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#best_value, best_probs, _ = current_player.get_preds(gs)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;31m#if any(i > 0.7 and i < 1 and np.sum(input_mat) < 20 for i in best_probs):\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.7\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_mat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m20\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mAV\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mgs_tmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mAV_tmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAV\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for m_idx, m in enumerate(memory.ltmemory):\n",
    "    gs = m['state']\n",
    "    AV = m['AV']\n",
    "    input_mat = current_player.model.convertToModelInput(gs)\n",
    "    \n",
    "    #best_value, best_probs, _ = current_player.get_preds(gs)\n",
    "    #if any(i > 0.7 and i < 1 and np.sum(input_mat) < 20 for i in best_probs):\n",
    "    if any(i > 0.7 and i < 1 and np.sum(input_mat) < 20 for i in AV):\n",
    "        gs_tmp = gs\n",
    "        AV_tmp = AV\n",
    "        print(m_idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PLAYER TURN: -1\n",
      "VALUE: -1\n",
      "POLICY: [0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.06578947 0.\n",
      " 0.78289474 0.         0.         0.01973684 0.         0.\n",
      " 0.         0.         0.         0.05921053 0.         0.\n",
      " 0.         0.01973684 0.         0.         0.         0.\n",
      " 0.02631579 0.         0.         0.         0.02631579 0.        ]\n"
     ]
    }
   ],
   "source": [
    "#idx = 573\n",
    "#idx = 1341\n",
    "idx = 171\n",
    "memory.ltmemory[idx]['state'].render(lg.logger_memory)\n",
    "print('PLAYER TURN: ' + str(memory.ltmemory[idx]['playerTurn']))\n",
    "print('VALUE: ' + str(memory.ltmemory[idx]['value']))\n",
    "print('POLICY: ' + str(memory.ltmemory[idx]['AV']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0 0 0 0 0 0 0]\n",
      "  [0 0 0 0 0 0 0]\n",
      "  [0 0 0 0 0 0 0]\n",
      "  [0 0 1 0 0 0 0]\n",
      "  [1 0 0 0 0 0 1]\n",
      "  [0 0 1 0 0 0 1]]\n",
      "\n",
      " [[0 0 0 0 0 0 0]\n",
      "  [0 0 0 0 0 0 0]\n",
      "  [0 0 0 0 0 0 0]\n",
      "  [0 0 0 0 1 0 0]\n",
      "  [0 0 1 0 1 0 0]\n",
      "  [1 0 0 1 1 0 0]]]\n",
      "[-0.30147693]\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.03493203 0.\n",
      " 0.8203545  0.         0.         0.02828824 0.         0.\n",
      " 0.         0.         0.         0.03300422 0.         0.\n",
      " 0.         0.02917367 0.         0.         0.         0.\n",
      " 0.02183691 0.         0.         0.         0.03241031 0.        ]\n",
      "{'playerTurn': -1, 'value': -1, 'state': <game.GameState instance at 0x1236dfe18>, 'board': array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "        0,  0,  0,  0,  0,  0, -1,  0,  1,  0,  0, -1,  0,  1,  0,  1,  0,\n",
      "       -1,  1,  0, -1,  1,  1,  0, -1]), 'AV': array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.06578947, 0.        , 0.78289474, 0.        ,\n",
      "       0.        , 0.01973684, 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.        , 0.05921053, 0.        , 0.        ,\n",
      "       0.        , 0.01973684, 0.        , 0.        , 0.        ,\n",
      "       0.        , 0.02631579, 0.        , 0.        , 0.        ,\n",
      "       0.02631579, 0.        ]), 'id': '000000000000000000000000010000101001001100000000000000000000000001000010000010010001'}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "gs = GameState(np.array([\n",
    "     0, 0, 0, 0, 0, 0, 0,\n",
    "     0, 0, 0, 0, 0, 0, 0,\n",
    "     0, 0, 0, 0, 0, 0, 0,\n",
    "     0, 0, 0, 1,-1, 0, 0,\n",
    "     0, 0, 0, 1,-1,-1, 0,\n",
    "     0, 0, 1, 1,-1,-1, 1\n",
    "]), 1)\n",
    "\n",
    "#current_NN = CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) + env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "\n",
    "#current_player = Agent('current_player', env.state_size, env.action_size, config.MCTS_SIMS, config.CPUCT, current_NN)\n",
    "\n",
    "# print(current_player.model.convertToModelInput(gs))\n",
    "# print(current_player.get_preds(gs))\n",
    "\n",
    "# print(best_player.model.convertToModelInput(gs))\n",
    "# print(best_player.get_preds(gs))\n",
    "gs = memory.ltmemory[idx]['state']\n",
    "best_value, best_probs, _ = current_player.get_preds(gs)\n",
    "print(current_player.model.convertToModelInput(gs)) #current_player\n",
    "print(best_value)\n",
    "print(best_probs)\n",
    "\n",
    "print(memory.ltmemory[idx])\n",
    "\n",
    "gs.render(lg.logger_memory)\n",
    "\n",
    "\n",
    "\n",
    "#current_player.replay(config.BATCH_SIZE * 2, memory.ltmemory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'playerTurn': 1, 'value': -1, 'state': <game.GameState instance at 0x1191343b0>, 'board': array([ 0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "        0,  0, -1,  0,  1,  0,  0, -1,  0,  1,  0, -1,  0,  0,  1,  0,  1,\n",
      "        0,  1, -1, -1, -1,  0,  1,  0]), 'AV': array([0.05      , 0.04285714, 0.06428571, 0.05714286, 0.69285714,\n",
      "       0.03571429, 0.05714286]), 'id': '000000000000000000000100001000010101000010000000000000000000010000100010000000111000'}\n"
     ]
    }
   ],
   "source": [
    "for i in memory.ltmemory:\n",
    "    if i['id'] == '000000000000000000000100001000010101000010000000000000000000010000100010000000111000':\n",
    "        print i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils import plot_model\n",
    "\n",
    "env = Game()\n",
    "state_size = len(env.gameState.binary)\n",
    "action_size = len(env.actionSpace)\n",
    "\n",
    "first_NN = Residual_CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) +  env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "first_player = Agent('first_player', state_size, action_size, config.MCTS_SIMS, config.CPUCT, model = first_NN)  #100\n",
    "#plot_model(first_player.model.model, to_file='model.png', show_shapes = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "pickle.dump( memory, open( \"./memory/memory_raw.p\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('player ', '1', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  1.]\n",
      "('player ', '4', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  3.]\n",
      "('player ', '7', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  5.]\n",
      "('player ', '10', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  7.]\n",
      "('player ', '13', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  9.]\n",
      "('player ', '16', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0. 11.]\n",
      "('player ', '19', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0. 13.]\n",
      "('player ', '22', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0. 14.]\n",
      "('player ', '25', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0. -2.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0. 16.]\n",
      "('player ', '28', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0. -2.  0.  0. -2.  0.  0.  0.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0. 18.]\n",
      "('player ', '31', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0.  0.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0. 20.]\n",
      "('player ', '34', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0. 22.]\n",
      "('player ', '37', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0.  0. 22.]\n",
      "('player ', '40', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0.  0.  0.  0. -1.  0.  0.  0.  0.  0.  0.  0.  0. 23.]\n",
      "('player ', '43', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0.  0.  0.  0. -1.  0.  0.  2.  0.  0.  0.  0.  0. 21.]\n",
      "('player ', '46', ' : ', 'player ', '49')\n",
      "1  1  [ 0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0. -2.  0.  0. -1.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.  0. -2.  0.\n",
      "  0.  0.  0.  0. -1.  0.  0.  2.  0.  0.  0.  0.  0. 21.]\n"
     ]
    }
   ],
   "source": [
    "env = Game()\n",
    "state_size = len(env.gameState.binary)\n",
    "action_size = len(env.actionSpace)\n",
    "tourney_scores = np.zeros(50)\n",
    "for i in np.arange(1,49,3):\n",
    "    player1_version = i\n",
    "    for j in [49]: #np.arange(i + 3, 49, 3):\n",
    "        player2_version = j\n",
    "        print('player ', str(player1_version), ' : ', 'player ', str(player2_version))\n",
    "        _, _, points, _ = playMatchesBetweenVersions(env, player1_version, player2_version, 1, lg.logger_tourney, 0, 1)\n",
    "        tourney_scores[player1_version] = tourney_scores[player1_version] + points['player1']\n",
    "        tourney_scores[player2_version] = tourney_scores[player2_version] + points['player2']\n",
    "        _, _, points, _ = playMatchesBetweenVersions(env, player1_version, player2_version, 1, lg.logger_tourney, 0, -1)\n",
    "        tourney_scores[player1_version] = tourney_scores[player1_version] + points['player1']\n",
    "        tourney_scores[player2_version] = tourney_scores[player2_version] + points['player2']\n",
    "        print(tourney_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1  2  3  4  5  6  7  8  9  10 "
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'drawn': 1, 'player1': 6, 'player2': 3},\n",
       " None,\n",
       " {'player1': [1, 1, 1, 1, 0, -1, 1, 1, -1, -1],\n",
       "  'player2': [-1, -1, -1, -1, 0, 1, -1, -1, 1, 1]},\n",
       " {'drawn': 1, 'nsp': 9, 'sp': 0})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "env = Game()\n",
    "playMatchesBetweenVersions(env, 28, 46, 10, lg.logger_tourney, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Enter your chosen action: 3\n",
      "Enter your chosen action: 3\n",
      "Enter your chosen action: 3\n",
      "Enter your chosen action: 4\n",
      "Enter your chosen action: 2\n",
      "Enter your chosen action: 0\n",
      "Enter your chosen action: 0\n",
      "Enter your chosen action: 6\n",
      "Enter your chosen action: 6\n",
      "Enter your chosen action: 6\n",
      " 2 "
     ]
    }
   ],
   "source": [
    "env = Game()\n",
    "state_size = len(env.gameState.binary)\n",
    "action_size = len(env.actionSpace)\n",
    "\n",
    "first_NN = Residual_CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) +  env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "\n",
    "first_player = Agent('first_player', state_size, action_size, config.MCTS_SIMS, config.CPUCT, model = first_NN)  #100\n",
    "user_player = User('player1', env.state_size, env.action_size)\n",
    "\n",
    "playMatches(user_player, first_player, 10, lg.logger_tourney, 0, memory = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4424797]\n"
     ]
    }
   ],
   "source": [
    "env = Game()\n",
    "state_size = len(env.gameState.binary)\n",
    "action_size = len(env.actionSpace)\n",
    "\n",
    "# gs = GameState(np.array([\n",
    "#      0, 0, 1,-1, 0,-1, 0,\n",
    "#      0, 0, 1, 1, 0, 1, 0,\n",
    "#      0, 0, 1,-1, 0,-1, 0,\n",
    "#      0, 0,-1, 1, 0, 1, 0,\n",
    "#     -1,-1, 1,-1, 0,-1, 0,\n",
    "#      1,-1, 1, 1,-1,-1, 0\n",
    "# ]), -1)\n",
    "\n",
    "gs = GameState(np.array([\n",
    "     0, 0, 0, 0, 0, 0, 0,\n",
    "     0, 0, 0, 0, 0, 0, 0,\n",
    "     0, 0, 0, 0, 0, 0, 0,\n",
    "     0, 0, 0, 1, 0, 0, 0,\n",
    "     0, 0, 0,-1, 0, 0, 0,\n",
    "     0, 0, 0, 1, 0, 0, 0\n",
    "]), -1)\n",
    "\n",
    "best_NN = Residual_CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) +  env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "m_tmp = best_NN.read(env.name, 31)\n",
    "best_NN.model.set_weights(m_tmp.get_weights())\n",
    "best_player = Agent('best_player', env.state_size, env.action_size, config.MCTS_SIMS, config.CPUCT, best_NN)\n",
    "\n",
    "preds = best_player.get_preds(gs)[0]\n",
    "\n",
    "print(preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3886329"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(best_player.mcts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'env' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-0dc741efc89f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbest_NN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mResidual_CNN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREG_CONST\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDROPOUT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLEARNING_RATE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m  \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid_shape\u001b[0m\u001b[0;34m,\u001b[0m   \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_size\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHIDDEN_CNN_LAYERS\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mm_tmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbest_NN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m31\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mbest_NN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mm_tmp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mbest_player\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAgent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'best_player'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maction_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMCTS_SIMS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCPUCT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_NN\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'env' is not defined"
     ]
    }
   ],
   "source": [
    "best_NN = Residual_CNN(config.REG_CONST, config.DROPOUT, config.LEARNING_RATE, (2,) +  env.grid_shape,   env.action_size + 1, config.HIDDEN_CNN_LAYERS)\n",
    "m_tmp = best_NN.read(env.name, 31)\n",
    "best_NN.model.set_weights(m_tmp.get_weights())\n",
    "best_player = Agent('best_player', env.state_size, env.action_size, config.MCTS_SIMS, config.CPUCT, best_NN)\n",
    "\n",
    "best_player.model.viewLayers()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_player_version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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